Effectiveness of COVID-19 vaccines against hospitalization and death in Canada: A multiprovincial test-negative design study
Bibliographic record
Abstract
ABSTRACT Background A major goal of COVID-19 vaccination is to prevent severe outcomes (hospitalizations and deaths). We estimated the effectiveness of mRNA and ChAdOx1 COVID-19 vaccines against severe outcomes in four Canadian provinces between December 2020 and September 2021. Methods We conducted this multiprovincial retrospective test-negative study among community-dwelling adults aged ≥18 years in Ontario, Quebec, British Columbia, and Manitoba using linked provincial databases and a common study protocol. Multivariable logistic regression was used to estimate province-specific vaccine effectiveness against COVID-19 hospitalization and/or death. Estimates were pooled using random effects models. Results We included 2,508,296 tested subjects, with 31,776 COVID-19 hospitalizations and 5,842 deaths. Vaccine effectiveness was 83% after a first dose, and 98% after a second dose, against both hospitalization and death (separately). Against severe outcomes (hospitalization or death), effectiveness was 87% (95%CI: 71%–94%) ≥84 days after a first dose of mRNA vaccine, increasing to 98% (95%CI: 96%–99%) ≥112 days after a second dose. Vaccine effectiveness against severe outcomes for ChAdOx1 was 88% (95%CI: 75%–94%) ≥56 days after a first dose, increasing to 97% (95%CI: 91%–99%) ≥56 days after a second dose. Lower one-dose effectiveness was observed for adults aged ≥80 years and those with comorbidities, but effectiveness became comparable after a second dose. Two doses of vaccines provided very high protection for both homologous and heterologous schedules, and against Alpha, Gamma, and Delta variants. Conclusions Two doses of mRNA or ChAdOx1 vaccines provide excellent protection against severe outcomes of hospitalization and death.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".